Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Glascock, Michael D.; Neff, Héctor; Vaughn, Kevin J.

doi:10.1023/b:hype.0000032025.37390.41

Cited by 112 publications

(87 citation statements)

References 13 publications

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“…This piece, from a painted ewer, was badly weathered, which may account for anomalous elemental concentrations. All results were converted from parts-per-million to log base-10 values and were processed using wellestablished statistical routines for ceramic compositional data (Baxter 2001;Dussubieux, et al 2007;Eerkens, et al 2002;Glascock, et al 2004;Kennett, et al 2004;Neff 1994;Niziolek 2013a, b). The first step in statistical analysis was to identify potential outliers to the dataset.…”

Section: Results and Discussion Of The Paste Analysismentioning

confidence: 99%

A Compositional Study of a Selection of Song Dynasty Chinese Ceramics From the Java Sea Shipwreck: Results From La-Icp-MS Analysis

Niziolek

2015

JIPA

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Section: Results and Discussion Of The Paste Analysismentioning

confidence: 99%

A Compositional Study of a Selection of Song Dynasty Chinese Ceramics From the Java Sea Shipwreck: Results From La-Icp-MS Analysis

Niziolek

2015

JIPA

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“…It comprises the bombardment of chemical elements in a sample with neutrons, producing artificial radioactive isotopes, which are identified and measured by the gamma radiation emitted in their decay. Many archaeometric studies employ INAA to determine the concentration of chemical elements in many materials due to its advantages, like the possibility of using small amounts of sample and high sensibility for trace elements (Guinn & Lukens, 1965;Bode, 1996;Glascock et al, 2004).…”

Section: Methodsmentioning

confidence: 99%

Neural Networks (SOM) Applied to INAA Data of Chemical Elements in Archaeological Ceramics from Central Amazon

Hazenfratz

Munita

Neves

2017

STAR: Science & Technology of Archaeological Research

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Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi-or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data. ARTICLE HISTORY

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“…The multivariate statistical analysis of chemical composition of archaeological ceramics is a very useful tool for determining the possible presence and provenance of the foreign ceramic styles [3][4][5][6][7], as well as for understanding the kind and grade of influence of wide-ranging civilizations on local groups. Given a representative sample of pottery of different styles from the same period in an archaeological site, the first step is thus to determine the elemental composition of each fragment in a complete and accurate way.…”

Section: Introductionmentioning

confidence: 99%

Wari influence in southern Peru: provenance study of middle horizon pottery from the archaeological site of La Real using k 0-INAA

Bedregal

Mendoza

Ubillús

et al. 2015

J Radioanal Nucl Chem

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Fragments of archaeological pottery from a rescue excavation at the site of La Real in Arequipa, Peru, were studied by instrumental neutron activation analysis, k 0 method. Analytical data were processed by multivariate statistical techniques, comparing the chemical composition of the studied samples versus the information available in our database on the chemical composition of archaeological pottery from Conchopata (Ayacucho), Cotahuasi (Arequipa), Huaro (Cusco) and Tiwanaku (Bolivia). The results obtained revealed that most of the samples were likely made locally at La Real, while others correspond to the chemical composition of the different groups considered, showing evidence of the presence of foreign pottery in the site and a small group which were not classified.

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Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Cited by 112 publications

References 13 publications

A Compositional Study of a Selection of Song Dynasty Chinese Ceramics From the Java Sea Shipwreck: Results From La-Icp-MS Analysis

A Compositional Study of a Selection of Song Dynasty Chinese Ceramics From the Java Sea Shipwreck: Results From La-Icp-MS Analysis

Neural Networks (SOM) Applied to INAA Data of Chemical Elements in Archaeological Ceramics from Central Amazon

Wari influence in southern Peru: provenance study of middle horizon pottery from the archaeological site of La Real using k 0-INAA

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